Predicting Marine Fuel with High Sulphur Content Using Machine Learning Algorithms

Abstract

Marine transportation is a significant source of air pollution especially around coastal areas with maritime vessels creating 12% of global sulphur oxides emission in 2014 alone. In compliance with International Maritime Organisation (IMO) regulations, the determination of sulphur content of marine fuels is typically carried out using lengthy laboratory-based analyses. The regulations prohibit the use of High-Sulphur Fuel Oil (HSFO) (>0.5% by weight of Sulphur) in Emission Control Areas (ECA). There is a need for a more efficient means of predicting Sulphur content and differentiating between HSFO and Very Low Sulphur Fuel Oil (VLSFO) samples. This study compares the application of a Support Vector Machine (SVM) and Agglomerative Hierarchical Clustering (AHC) algorithm enhanced with Principal Component Analysis for dimensionality reduction purposes to predict HSFO and VLSFO marine fuel samples based on near-infrared (NIR) industrial data from North Sea operations correlated with laboratory-measured sulphur values instead of relying on lengthy laboratory-based measurements. The study also compares the effect of normalising the data by setting the area under the curve to one and standardising it by subtracting the mean of predictor variables and scaling by standard deviation. The results show that although >70% of HSFO samples were accurately predicted with the SVM, a better result was achieved using the unsupervised learning approach of AHC/PCA with >80% of HSFO samples correctly predicted despite the imbalance in the industrial data providing an effective model for the rapid and well-informed decision-making tool for vessel operators. Normalising the area under the curve to one produced similar results to using standardised data.

Keywords

Machine learning / Marine fuel / Support vector machines / Agglomerative hierarchical clustering / High sulphur fuel oil / Very low sulphur fuel oil

References

[1] Ahmad H, Dang S (2015) Performance Evaluation of Clustering Algorithm Using Different Dataset. International Journal of Advance Research in Computer Science and Management Studies, 8

[2] Al Ibrahim E, Farooq A. Prediction of the Derived Cetane Number and Carbon/Hydrogen Ratio from Infrared Spectroscopic Data. Energy & Fuels, 2021, 35(9): 8141-8152.

[3] Awad M, Khanna R (2015) Support Vector Machines for Classification. Efficient Learning Machines, 39–66

[4] Bangert P (2021) 3.3.3 Support Vector Machines. Machine Learning and Data Science in the Oil and Gas Industry-Best Practices, Tools, and Case Studies, 48–49

[5] Bekkar M, Djemaa HK, Alitouche TA (2013) Evaluation Measures for Models Assessment over Imbalanced Data Sets. J Inf Eng Appl 3(10)

[6] Bertsekas DPConstrained Optimization and Lagrange Multiplier Methods, 2014

[7] Bilgili L. Life Cycle Comparison of Marine Fuels for Imo 2020 Sulphur Cap. Science of The Total Environment, 2021, 774: 145719.

[8] Blanco M, Villarroya I. Nir Spectroscopy: A Rapid-Response Analytical Tool. TrAC Trends in Analytical Chemistry, 2002, 21(4): 240-250.

[9] Broadhurst DI, Kell DB. Statistical Strategies for Avoiding False Discoveries in Metabolomics and Related Experiments. Metabolomics, 2006, 2(4): 171-196.

[10] Christopher J, Patel MB, Ahmed S, Basu B. Determination of Sulphur in Trace Levels in Petroleum Products by Wavelength-Dispersive X-Ray Fluorescence Spectroscopy. Fuel, 2001, 80(13): 1975-1979.

[11] Ciaburro G, Joshi P1.6 Normalization, 20192nd Edition

[12] ConcaweMarine Fuel Facts, 2022, 2016(10 November)

[13] Corbett JJ, Winebrake JJ, Green EH, Kasibhatla P, Eyring V, Lauer A. Mortality from Ship Emissions: A Global Assessment. Environmental Science & Technology, 2007, 41(24): 8512-8518.

[14] Cortes C, Vapnik V. Support-Vector Networks. Machine learning, 1995, 20(3): 273-297.

[15] Cullinane K, Bergqvist R. Emission Control Areas and Their Impact on Maritime Transport. Transportation Research Part D: Transport and Environment, 2014, 28: 1-5.

[16] Dadi HS, Pillutla GM. Improved Face Recognition Rate Using Hog Features and Svm Classifier. IOSR Journal of Electronics and Communication Engineering, 2016, 11(04): 34-44.

[17] Deng F, Guo S, Zhou R, Chen J. Sensor Multifault Diagnosis with Improved Support Vector Machines. IEEE transactions on automation science and engineering, 2015, 14(2): 1053-1063.

[18] Everitt BS, Dunn G. 6.2 Agglomerative Hierarchical Clustering Techniques. Applied Multivariate Data Analysis, 20012nd Edition.

[19] Eyring V, Isaksen ISA, Berntsen T, Collins WJ, Corbett JJ, Endresen O, Grainger RG, Moldanova J, Schlager H, Stevenson DS. Transport Impacts on Atmosphere and Climate: Shipping. Atmospheric Environment, 2010, 44(37): 4735-4771.

[20] Fan L, Shen H, Yin J. Mixed Compliance Option Decisions for Container Ships under Global Sulphur Emission Restrictions. Transportation Research Part D: Transport and Environment, 2023, 115: 103582.

[21] Fanali S, Haddad PR, Poole CF, Riekkola M-L. 21.3.3 Normalization. Liquid Chromatography-Fundamentals and Instrumentation, Volume 1, 20172nd Edition

[22] Gelbart MA, Snoek J, Adams RP (2014) Bayesian Optimization with Unknown Constraints. arXiv preprint arXiv: 1403.5607. https://doi.org/10.48550/arXiv.1403.5607

[23] Gu Y, Wang Y, Iris Ç. Integrated Green Technology Adoption, Ship Speed Optimization and Slot Management for Shipping Alliance under Emission Limits and Uncertain Fuel Prices. Journal of Cleaner Production, 2025, 494: 144939.

[24] Gunn SR. Support Vector Machines for Classification and Regression. ISIS technical report, 1998, 14(1): 5-16

[25] Hassellöv IM, Turner DR, Lauer A, Corbett JJ. Shipping Contributes to Ocean Acidification. Geophysical Research Letters, 2013, 40(11): 2731-2736.

[26] He H, Garcia EA. Learning from Imbalanced Data. IEEE Transactions on knowledge and data engineering, 2009, 21(9): 1263-1284.

[27] Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B. Support Vector Machines. IEEE Intelligent Systems and their applications, 1998, 13(4): 18-28.

[28] Huang J, Romero-Torres S, Moshgbar M (2010) Practical Considerations in Data Pre-Treatment for Nir and Raman Spectroscopy, American Pharmaceutical Review. Dostopno na: http://www.americanpharmaceuticalreview.com/Featured-Articles/116330-Practical-Considerations-in-Data-Pre-treatment-for-NIR-and-Raman-Spectroscopy/. [Dostop: 10-Sep-2019]

[29] IHMMarineSurveysFuel Oil Sulphur Testing and Analysis, 2023, 2020(20 July)

[30] Ju H-j, Jeon S-k. Effect of Ultrasound Irradiation on the Properties and Sulfur Contents of Blended Very Low-Sulfur Fuel Oil (Vlsfo). Journal of Marine Science and Engineering, 2022, 10(7): 980.

[31] Kapoutsis E, Theodoulidis B, Saraee M. Svm Categorizer: A Generic Categorization Tool Using Support Vector Machines. Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications, 20241109-1112

[32] Kuzu SL, Bilgili L, Kiliç A. Estimation and Dispersion Analysis of Shipping Emissions in Bandirma Port, Turkey. Environment, Development and Sustainability, 2021, 23(7): 10288-10308.

[33] Lammoglia T, de Souza Filho CR. Spectroscopic Characterization of Oils Yielded from Brazilian Offshore Basins: Potential Applications of Remote Sensing. Remote Sensing of Environment, 2011, 115(10): 2525-2535.

[34] Lantz B10.1.5 Visualizing Performance Tradeoffs with Roc Curves, 20193rd Edition331-332

[35] Lantz B. Machine Learning with R. Learn Techniques for Building and Improving Machine Learning Models, from Data Preparation to Model Tuning, Evaluation, and Working with Big Data, 20234th Edition

[36] Li H, Chen H, Li Y, Chen Q, Fan X, Li S, Ma M. Prediction of the Optical Properties in Photonic Crystal Fiber Using Support Vector Machine Based on Radial Basis Functions. Optik, 2023, 275: 170603.

[37] Liping W, Xuelong H, Jiang N. Robust Time Delay Estimation Based on Asinh Transform under α-Stable Noises. 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), 2017162-166

[38] Liu YPython Machine Learning by Example, 20203rd Edition

[39] Maklin CHierarchical Agglomerative Clustering Algorithm: Example in Python, 2021, 2018(21 July)

[40] MathworksFitcsvm, 2022, 2022(June 06)

[41] Mehta S, Kundra D. Combining Cnn and Svm for Robust Cattle Disease Classification in Veterinary Applications. 2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), 20241-5

[42] Meyer D, Leisch F, Hornik K. The Support Vector Machine under Test. Neurocomputing, 2003, 55(1–2): 169-186.

[43] Murtagh F, Contreras P. Algorithms for Hierarchical Clustering: An Overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2012, 2(1): 86-97

[44] Nagla JRStatistics for Textile Engineer, 2014

[45] NCSSHierarchical Clustering/Dendrograms, 2021, 2021(09 September)

[46] Patel KA, Thakral P. The Best Clustering Algorithms in Data Mining. 2016 International Conference on Communication and Signal Processing (ICCSP), 20162042-2046.

[47] Saldana DA, Starck L, Mougin P, Rousseau B, Ferrando N, Creton B. Prediction of Density and Viscosity of Biofuel Compounds Using Machine Learning Methods. Energy & fuels, 2012, 26(4): 2416-2426.

[48] Sandak J, Sandak A, Meder R. Assessing Trees, Wood and Derived Products with near Infrared Spectroscopy: Hints and Tips. Journal of Near Infrared Spectroscopy, 2016, 24(6): 485-505.

[49] Spackman KA. Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning. Proceedings of the sixth international workshop on Machine learning, 1989160-163.

[50] Sreedhar Kumar S, Madheswaran M, Vinutha B, Manjunatha Singh H, Charan K. A Brief Survey of Unsupervised Agglomerative Hierarchical Clustering Schemes. Int J Eng Technol (UAE), 2019, 8(1): 29-37

[51] Stratiev D, Dinkov R, Petkov K, Stanulov K. Evaluation of Crude Oil Quality. Petroleum & Coal, 2010, 52(1): 35-43

[52] Sun D-W. 4.3 Evaluation of Classification Performances. Infrared Spectroscopy for Food Quality Analysis and Control, 2009

[53] Thijssen P, Hadjiloucas S. 12.3.2 Advances in Support Vector Machine Classifiers. State Estimation in Chemometrics-the Kalman Filter and Beyond, 20202nd Edition237

[54] Van TC, Ramirez J, Rainey T, Ristovski Z, Brown RJ. Global Impacts of Recent Imo Regulations on Marine Fuel Oil Refining Processes and Ship Emissions. Transportation Research Part D: Transport and Environment, 2019, 70: 123-134.

[55] Wang H, Hu L, Zhang Y. Svm Based Imbalanced Correction Method for Power Systems Transient Stability Evaluation. ISA Transactions, 2023, 136: 245-253.

[56] Wang Q, Chen D, Li M, Li S, Wang F, Yang Z, Zhang W, Chen S, Yao D. A Novel Method for Petroleum and Natural Gas Resource Potential Evaluation and Prediction by Support Vector Machines (Svm). Applied Energy, 2023, 351: 121836.

[57] Westerhuis JA, Hoefsloot HC, Smit S, Vis DJ, Smilde AK, van Velzen EJ, van Duijnhoven JP, van Dorsten FA. Assessment of Plsda Cross Validation. Metabolomics, 2008, 4(1): 81-89.

[58] Workman JHandbook of Organic Compounds: Nir, Ir, Raman and Uv-Vis Spectra Featuring Polymers and Surfactants (a 3-Volume Set). 3. Ir and Raman Spectra, 2001

[59] Zhang N, Wei M, Bai B, Wang X, Hao J, Jia S. Pattern Recognition for Steam Flooding Field Applications Based on Hierarchical Clustering and Principal Component Analysis. ACS Omega, 2022, 7(22): 18804-18815.

[60] Zhang T. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Ai Magazine, 2001, 22(2): 103-103

[61] Zis TP, Cullinane K. The Desulphurisation of Shipping: Past, Present and the Future under a Global Cap. Transportation Research Part D: Transport and Environment, 2020, 82: 102316.

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